A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma

Neuroblastoma is a leading cause of childhood cancer mortality, presenting persistent management challenges due to its biological heterogeneity and the limited accessibility of molecular profiling in routine practice. Here we present NEVA (NEuroblastoma Vision-language AI), a multimodal foundation model designed to address these barriers. Unlike conventional approaches that rely on frozen encoders and multiple instance learning, NEVA implements a pathologist-inspired hierarchical workflow with end-to-end optimization. Developed and evaluated in a large multi-institutional cohort of 1,238 patients across multiple centers, NEVA outperforms ten representative foundation models, including TITAN, UNI, and Virchow, across the majority of the 11 clinical tasks evaluated. The model demonstrates diagnostic capability, achieving Area Under the Receiver Operating Characteristic curves of 0.916 for subtype classification, 0.823 for Shimada classification, and 0.806 for risk group stratification. Furthermore, NEVA predicts key molecular alterations from routinely available pathology data, reaching an AUROC of 0.924 for NMYC amplification and 0.830 for 1p36 deletion, while enabling prognostic stratification for progression-free and overall survival across multiple test cohorts. By integrating interpretable attention maps that localize histologically relevant regions, NEVA establishes a scalable framework for neuroblastoma risk stratification and clinical decision support.